Long-term exposure to PM2.5 and cardiorespiratory mortality: an ecological small-area study in five cities in Colombia

Diana Marín, Víctor Herrera, Juan Gabriel Piñeros-Jiménez, Oscar Alberto Rojas-Sánchez, Sonia C. Mangones, Yurley Rojas, Jhon Cáceres, Dayana M. Agudelo-Castañeda, Néstor Y. Rojas, Luis Carlos Belalcazar-Ceron, Jonathan Ochoa-Villegas, María Leonor Montes-Mejía, Veronica Maria Lopera-Velasquez, Sanit María Castillo-Navarro, Alexander Torres-Prieto, Jill Baumgartner, Laura A. Rodríguez-Villamizar

Producción científica: Contribución a una revistaArtículo en revista científica indexadarevisión exhaustiva

Resumen

Long-term exposure to the fine particulate matter (PM2.5) is a risk factor for cardiorespiratory mortality. However, little is known about its distribution and health impact in large cities in low-middle-income countries where population exposure has increased during the last decades. This ecological study evaluated the association between PM2.5 concentration and adult cardiorespiratory mortality at the intraurban census sector (CS) level of Colombia's five most populated cities (2015-2019). We estimated incidence rate ratios (IRR; per 5µg/m3) by fitting negative binomial regressions to smoothed Bayesian mortality rates (BMR) on PM2.5 predicted from land use regression (LUR) models, adjusting for CS demographic structure, multidimensional poverty index, and spatial autocorrelation. CS median PM2.5 ranged from 8.1µg/m3 in Bucaramanga to 18.7µg/m3 in Medellín, whereas Bogotá had the highest variability (IQR = 29.5µg/m3) and cardiorespiratory mortality (BMR = 2,560 per 100,000). Long-term exposure to PM2.5 increased cardiorespiratory mortality in Bucaramanga (IRR = 1.15; 95%CI: 1.02; 1.31), without evidence of spatial clustering, and cardiovascular (IRR = 1.06; 95%CI: 1.01; 1.12) and respiratory (IRR = 1.07; 95%CI: 1.02; 1.13) mortality in Medellín. Cardiorespiratory mortality spatially clustered in some Colombian cities and was associated with long-term exposure to PM2.5 in urban areas where the LUR models had the highest predictive accuracy. These findings highlight the need to incorporate high-quality, high-resolution exposure assessments to better understand the health impact of air pollution and inform public health interventions in urban environments.
Idioma originalInglés
Páginas (desde-hasta)e00071024
PublicaciónCadernos de Saude Publica
Volumen41
N.º4
DOI
EstadoPublicada - abr. 2025

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